A Fully Customized Baseline Removal Framework for Spectroscopic Applications

Area of Science:

• Spectroscopy
• Chemometrics
• Data Analysis

Background:

• Baseline removal is crucial in spectroscopy for accurate feature analysis.
• Current methods often rely on default parameters or are application-specific.
• Lack of generalizability limits the effectiveness of existing baseline correction techniques.

Purpose of the Study:

• To develop a generalized method for baseline removal in spectroscopy.
• To create novel algorithms tailored to specific spectroscopic techniques and datasets.
• To enhance the predictive accuracy of spectroscopic models through automated baseline correction.

Main Methods:

• The Custom Baseline Removal (Custom BLR) method combines operations from existing algorithms.
• Novel algorithms are synthesized for each specific technique, application, and training set.
• The method discovers algorithms that maximize predictive accuracy of spectroscopic models.

Main Results:

• Custom BLR methods achieve performance matching or exceeding existing alternatives.
• Demonstrated success in near-infrared spectroscopy (corn), laser-induced breakdown spectroscopy (rocks), and Raman spectroscopy (minerals).
• The learned methods significantly improve quantification and classification tasks.

Conclusions:

• Custom BLR offers a significant advancement toward automatic and general baseline removal.
• The method reduces subjectivity in data preprocessing for spectroscopy.
• Applicable to various spectroscopic fields and potentially other data analysis domains.